Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Surface electromyogram (sEMG) signals are crucial for human upper limb force estimation and motion intention recognition.
• Electrocardiogram (ECG) artifacts significantly degrade sEMG signal quality, especially near the heart, hindering accurate analysis.
• Existing methods struggle to effectively remove these interfering ECG artifacts.

Purpose of the Study:

• To develop and validate a novel method for the effective removal of ECG artifacts from sEMG signals.
• To enhance the quality of sEMG signals contaminated by ECG interference.
• To improve the accuracy of sEMG-based applications such as force estimation and motion recognition.

Main Methods:

• Acquisition of raw sEMG and ECG signals from upper limb muscles (biceps brachii, brachialis, triceps).
• Preprocessing using an improved multi-layer wavelet transform to remove background noise and power frequency interference.
• Application of an improved Fast-Independent Component Analysis (Fast-ICA) algorithm for signal component separation.
• Utilizing an ECG discrimination algorithm for artifact recognition and elimination.

Main Results:

• The proposed method demonstrates significant effectiveness in removing ECG artifacts from contaminated sEMG signals.
• Experimental results confirm a substantial improvement in the overall quality of the processed sEMG signals.
• The technique shows promise for enhancing the accuracy of force estimation and motion intention recognition tasks.

Conclusions:

• The novel method provides a robust solution for ECG artifact removal in sEMG recordings.
• Improved sEMG signal quality has significant implications for reliable human-machine interface applications.
• This approach offers valuable insights for processing other biological signals contaminated by artifacts.